Investments in ERP and other business applications have driven substantial productivity improvements in the past two decades.
Since 2004, the median number of full-time employees in the finance department at big companies has declined 40% to about 71 people for every $1 billion of revenue, down from 119, according to Hackett Group a consulting firm.
Large firms employ 44% fewer full-time information technology workers and 20% fewer human resource workers than they did a decade ago, according to Hackett, at least in part because automation has cut the number of employees.
While these benefits are pretty substantial, the marginal returns on further investments in centralization and process efficiencies from optimization of manual labor continue to decline. The low hanging fruit is already gone. For companies to drive the next wave of improvement, many are increasingly turning to the promise of new generation enablers such as Artificial Intelligence, Robotic Process Automation (RPA) and machine learning. Indeed, technologies such as these are already pervasive in the digital world driving dramatic improvements in the areas of manufacturing, consumer, retail, healthcare and many others.
But the use of such techniques in corporate back office and administrative ERP functions is still in its infancy. The finance department remains much as it was a decade ago. True, most people now have tablets and laptops instead of desktop computers, online versus paper reports and modern versions of Excel. But most other things in the office are not a surprise.
But powerful new forces have begun to change the world of back office. Finance, procurement, order management and many other administrative ERP functions are now in the early stages of transformation comparable to the one that reshaped
The availability of algorithmic techniques to automate transaction processing is not new. What is new if the availability of larger data sets to train the machines, cheap compute in the cloud, improvements in sensors and speed of compute (in-memory) to make the output actionable. Some examples use cases of how AI and intelligent automation are listed below:
AI can expedite exception handling in many financial processes. For example, when a payment is received without a corresponding purchase order, significant manual effort is required to identify the right PO and whether the payment amount was correct.
Intelligent systems can ingest invoices at high volume, apply natural language processing (NLP) techniques to read and assess markers that can identify to the right order and invoice. 80% of incoming payments can be processed this way and the rest flagged for human review because something doesn’t match up.
Model and score credit of business entities based on history and 3rd party indicators more frequently to proactively increase or decrease credit limits to increase revenues that might otherwise be lost and reduce bad debt.
A machine learning model that can predict any aspect of a logistic delivery system based on the data that it has available on finished good inventory – origins, transit routes, times when it is scanned or its location and status are reported by RF (Radio Frequency) tags etc. - can be used to trigger a remedial action for a human to review. Companies that can take advantage of the data already present in the supply chain stand to gain against the competition due to enhanced customer service.
The race for harnessing the disruptive potential of Artificial Intelligence is on. A recent McKinsey report (Artificial Intelligence, The Next Digital Frontier) illustrated how firms that combine strong digital capability, robust AI adoption and a proactive AI strategy foresee an outsized financial performance.
Whether it's the lure for more profits or the need to stay ahead of competitive disruption, its clear AI is here to stay. The future of ERP is likely to be no different. Intelligent ERP is the only way forward for any company thinking about modernizing its ERP footprint.
Since 2004, the median number of full-time employees in the finance department at big companies has declined 40% to about 71 people for every $1 billion of revenue, down from 119, according to Hackett Group a consulting firm.
Large firms employ 44% fewer full-time information technology workers and 20% fewer human resource workers than they did a decade ago, according to Hackett, at least in part because automation has cut the number of employees.
While these benefits are pretty substantial, the marginal returns on further investments in centralization and process efficiencies from optimization of manual labor continue to decline. The low hanging fruit is already gone. For companies to drive the next wave of improvement, many are increasingly turning to the promise of new generation enablers such as Artificial Intelligence, Robotic Process Automation (RPA) and machine learning. Indeed, technologies such as these are already pervasive in the digital world driving dramatic improvements in the areas of manufacturing, consumer, retail, healthcare and many others.
But the use of such techniques in corporate back office and administrative ERP functions is still in its infancy. The finance department remains much as it was a decade ago. True, most people now have tablets and laptops instead of desktop computers, online versus paper reports and modern versions of Excel. But most other things in the office are not a surprise.
But powerful new forces have begun to change the world of back office. Finance, procurement, order management and many other administrative ERP functions are now in the early stages of transformation comparable to the one that reshaped
The availability of algorithmic techniques to automate transaction processing is not new. What is new if the availability of larger data sets to train the machines, cheap compute in the cloud, improvements in sensors and speed of compute (in-memory) to make the output actionable. Some examples use cases of how AI and intelligent automation are listed below:
- Automating finance
AI can expedite exception handling in many financial processes. For example, when a payment is received without a corresponding purchase order, significant manual effort is required to identify the right PO and whether the payment amount was correct.
Intelligent systems can ingest invoices at high volume, apply natural language processing (NLP) techniques to read and assess markers that can identify to the right order and invoice. 80% of incoming payments can be processed this way and the rest flagged for human review because something doesn’t match up.
- Automated credit changes
Model and score credit of business entities based on history and 3rd party indicators more frequently to proactively increase or decrease credit limits to increase revenues that might otherwise be lost and reduce bad debt.
- Predicting late deliveries
A machine learning model that can predict any aspect of a logistic delivery system based on the data that it has available on finished good inventory – origins, transit routes, times when it is scanned or its location and status are reported by RF (Radio Frequency) tags etc. - can be used to trigger a remedial action for a human to review. Companies that can take advantage of the data already present in the supply chain stand to gain against the competition due to enhanced customer service.
The race for harnessing the disruptive potential of Artificial Intelligence is on. A recent McKinsey report (Artificial Intelligence, The Next Digital Frontier) illustrated how firms that combine strong digital capability, robust AI adoption and a proactive AI strategy foresee an outsized financial performance.
Whether it's the lure for more profits or the need to stay ahead of competitive disruption, its clear AI is here to stay. The future of ERP is likely to be no different. Intelligent ERP is the only way forward for any company thinking about modernizing its ERP footprint.
- SAP Managed Tags:
